{"type":"ScholarlyArticle","title":"Robust Counterfactual Explanations in Machine Learning: A Survey","authors":[{"name":"Junqi Jiang","slug":"junqi-jiang","given":"Junqi","family":"Jiang","affiliation":"Department of Computing , Imperial College London , UK"},{"name":"Francesco Leofante","slug":"francesco-leofante","given":"Francesco","family":"Leofante","affiliation":"Department of Computing , Imperial College London , UK"},{"name":"Antonio Rago","slug":"antonio-rago","given":"Antonio","family":"Rago","affiliation":"Department of Computing , Imperial College London , UK"},{"name":"Francesca Toni","slug":"francesca-toni","given":"Francesca","family":"Toni","affiliation":"Department of Computing , Imperial College London , UK"}],"abstract":"Counterfactual explanations (CEs) are advocated as being ideally suited to providing algorithmic recourse for subjects affected by the predictions of machine learning models. While CEs can be beneficial to affected individuals, recent work has exposed severe issues related to the robustness of state-of-the-art methods for obtaining CEs. Since a lack of robustness may compromise the validity of CEs, techniques to mitigate this risk are in order. In this survey, we review works in the rapidly growing area of robust CEs and perform an in-depth analysis of the forms of robustness they consider. We also discuss existing solutions and their limitations, providing a solid foundation for future developments.","doi":"10.24963/ijcai.2024/894","url":"https://ijcai.org/proceedings/2024/894","pdf":"https://ijcai.org/proceedings/2024/0894.pdf","markdown":"https://ijcai.org/proceedings/2024/894.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/894","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Survey Track","track":"Survey Track","number":894,"pages":{"first":8086,"last":8094},"keywords":["AI Ethics, Trust, Fairness: ETF: Explainability and interpretability","Machine Learning: ML: Robustness","Machine Learning: ML: Trustworthy machine learning","AI Ethics, Trust, Fairness: ETF: Trustworthy AI","AI Ethics, Trust, Fairness: ETF: Safety and robustness","Machine Learning: ML: Explainable/Interpretable machine learning"],"videos":[],"references":[{"position":1,"raw":"[Artelt et al., 2021] André Artelt, Valerie Vaquet, Riza Veli- oglu, Fabian Hinder, Johannes Brinkrolf, Malte Schilling, and Barbara Hammer. 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